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Keywords
(19)
Acoustic Modeling
bayesian learning
bayesian method
Condition Dependence
Conditional Independence
Covariance Matrix
Discriminative Training
Feature Vector
Gaussian Mixture Model
Indexing Terms
Likelihood Function
Map Estimation
Marginal Likelihood
Relevance Vector Machine
Speech Recognition
State Dependence
Hidden Markov Model
Maximum Likelihood
Markov Model
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Bayesian sensing hidden Markov models for speech recognition
Bayesian sensing hidden Markov models for speech recognition,10.1109/ICASSP.2011.5947493,George Saon,JenTzung Chien
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Bayesian sensing hidden Markov models for speech recognition
(
Citations: 1
)
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George Saon
,
JenTzung Chien
We introduce Bayesian sensing hidden Markov models (BSHMMs) to represent speech data based on a set of statedependent basis vectors. By incorporating the prior density of sensing weights, the relevance of a
feature vector
to different bases is determined by the corresponding precision parameters. The BSHMM parameters, consisting of the basis vectors, the precision matrices of sensing weights and the precision matrices of reconstruction errors, are jointly estimated by maximizing the likelihood function, which is marginalized over the weight priors. We derive recursive solutions for the three parameters, which are expressed via maximum a posteriori estimates of the sensing weights. Experimental results on an LVCSR task show consistent gains over conventional HMMs with
Gaussian mixture
models for both ML and
discriminative training
scenarios. Index Terms— Speech recognition, Bayesian learning, basis representation, acoustic model buried Markov models [5] which relaxed the
conditional independence
assumption for the representation of speech features. A set of statedependent basis vectors was trained to express the conditionally dependent feature vectors. In yet another approach, subspace
Gaussian mixture
models [6] were constructed to represent speech features by using the statedependent weights and a common largescale GMM structure. The feature representation was seen as sensing based on different subspaces of a global GMM. In this study, we address the basis representation of speech features for hidden Markov modeling and present the Bayesian sensing framework to ensure model regularization for speech recognition. The resulting BSHMMs are constructed by a set of basis vectors, the precision matrix of sensing weights, and the precision matrix of reconstruction errors. The precision matrix of weights naturally reflects how relevant the input feature is encoded by the basis vectors similar to the perspective of
relevance vector machine
(RVM) [7]. Importantly, we maximize the
marginal likelihood
of the training data over random weights and jointly estimate the three sets of parameters. Multivariate solutions are derived by
maximum likelihood
(ML) type II estimation and expressed through recursive formulas. These formulas are interpreted in terms of the mean vector and
covariance matrix
of the a posteriori distribution of the sensing weights. The maximum a posteriori (MAP) estimate of the sensing weights plays a central role in BSHMMs. Experimental results on an LVCSR task show consistent improvements over standard HMMs with
Gaussian mixture
models.
Conference:
International Conference on Acoustics, Speech, and Signal Processing  ICASSP
, pp. 50565059, 2011
DOI:
10.1109/ICASSP.2011.5947493
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Citation Context
(1)
...Undeterred by this state of affairs, we experiment with discriminative training for a new class of acoustic models called Bayesian sensing HMMs [
6
] which combine ideas from relevance vector machines [7] and bayesian dictionary learning...
...In [
6
], we discuss the estimation of BSHMM parameters according to the ML type II criterion by maximizing the marginal likelihood of the training data X = {xt}...
...It is noteworthy that the ML type II solutions for Φi and Ri are obtained as a special case of (9) and (11) by setting γ den t (i) and Di to zero as shown in the companion paper [
6
]...
George Saon
,
et al.
Discriminative training for Bayesian sensing hidden Markov models
References
(11)
Bayesian compressive sensing for phonetic classification
(
Citations: 10
)
Tara N. Sainath
,
Avishy Carmi
,
Dimitri Kanevsky
,
Bhuvana Ramabhadran
Conference:
International Conference on Acoustics, Speech, and Signal Processing  ICASSP
, pp. 43704373, 2010
Sparse coding for speech recognition
(
Citations: 4
)
Garimella S. V. S. Sivaram
,
Sridhar Krishna Nemala
,
Mounya Elhilali
,
Trac D. Tran
,
Hynek Hermansky
Conference:
International Conference on Acoustics, Speech, and Signal Processing  ICASSP
, pp. 43464349, 2010
Predictive hidden Markov model selection for speech recognition
(
Citations: 16
)
Jentzung Chien
,
Sadaoki Furui
Journal:
IEEE Transactions on Speech and Audio Processing  IEEE SAP
, vol. 13, no. 3, pp. 377387, 2005
Pattern Recognition and Machine Learning
(
Citations: 2395
)
C. Bishop
Published in 2006.
Buried Markov models for speech recognition
(
Citations: 45
)
Jeff A. Bilmes
Conference:
International Conference on Acoustics, Speech, and Signal Processing  ICASSP
, vol. 2, pp. 713716 vol.2, 1999
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Citations
(1)
Discriminative training for Bayesian sensing hidden Markov models
(
Citations: 1
)
George Saon
,
JenTzung Chien
Conference:
International Conference on Acoustics, Speech, and Signal Processing  ICASSP
, pp. 53165319, 2011